PROSPECTS AND POSSIBILITIES OF IMPLEMENTING BIO-INSPIRED AI TECHNOLOGIES IN THE MODEL OF MANAGEMENT OF REMEDIATION OF WAR-AFFECTED TERRITORIES

Bohdan Cherniavskyi ()
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Bohdan Cherniavskyi: Faculty of Economics and Technical Sciences, University of Applied Sciences in Konin, Konin, Przyjażni 1, 62-510, Poland

Review of Economic and Business Studies, 2025, issue si, 49-66

Abstract: This article explores the theoretical and practical aspects of applying bio-inspired algorithms in an optimization model for managing the remediation of war-affected areas. The author analyzes the dialectical relationship between the increasing complexity, changing nature, and scale of the impact of military conflicts and the modernization of approaches to remediation. This modernization enables a better adaptation to evolving situations and the selection of optimal, environmentally conscious, and innovative solutions. The implementation of unconventional approaches becomes crucial in scenarios where military conflicts result in phenomena such as environmental catastrophe, ecocide, genocide, and urbicide, which require the coordination of a large number of agents and a more extensive, integrated approach. Bio-inspired algorithms, with their ability to adapt to changing conditions and process large datasets, can optimize remediation management processes. The author examines how integrating bio-inspired approaches such as Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO) with spatial analysis technologies like Geographic Information Systems (GIS) can improve forecasting accuracy, enhance resource allocation, and accelerate the recovery of war-affected areas.

Keywords: remediation; bio-inspired AI technologies; Ant Colony Optimization (ACO); Artificial Bee Colony (ABC); Particle Swarm Optimization(PSO) (search for similar items in EconPapers)
JEL-codes: H56 O44 Q01 Q56 Q58 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aic:revebs:y:2025:j:si:cherniavskyib

DOI: 10.47743/rebs-2025-si-0004

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